The long-range goal of this project is to do research on a biomedical knowledge-based system for interpreting the clinical significance of physiological data measured by biomedical sensing and data acquisition systems. This interpretation will be used to aid in diagnostic decision making and the selection of therapeutic action. Even the best measurements often go unused because of the reasonable reluctance of clinical staff to make measurements whose results they only poorly understood and whose relation to clinical management is ambiguous. We will use techniques of biomedical "knowledge engineering" extracting and systematizing the heuristic knowledge used by experts in the practice of their art. These techniques will be used to construct and utilize a knowledge base to guide inference-making by computer programs. The immediate clinical objectives of opportunity involve the interpretation of measures of pulmonary function in the laboratory and the intensive care unit (ICU). These objectives represent clinically useful physiological measurements taken by biomedical sensing and data acquisition systems. Such a system is available and provides large quantities of well documented data on a routine basis. The computer programs for interpretation, analysis and diagnosis will be based on extensions of successful knowledge-based computer programs. These extensions will include heuristic and mathematical models to characterize common prototype models of abnormal physiology and procedures for farming hypotheses about expected changes in the measured data from the continually evolving patient physiological state. A collaborative effort is proposed which links the experience at The Institutes of Medical Sciences in biomedical engineering and diagnosis and management of respiratory insufficiency with expertise at Stanford University in research on and application of knowledge-based computer programs.